Chai, Soo See and Cheah, Whye Lian and Kok, Luong Goh and Chang, Robin Yee Hui and Kwan, Yong Sim and Chin, Kim On (2021) A Multilayer Perceptron Neural Network Model to Classify Hypertension in Adolescents Using Anthropometric Measurements: A CrossSectional Study in Sarawak, Malaysia. Computational and Mathematical Methods in Medicine, 2021. pp. 111. ISSN 09530460
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Abstract
This study outlines and developed a multilayer perceptron (MLP) neural network model for adolescent hypertension classification focusing on the use of simple anthropometric and sociodemographic data collected from a crosssectional research study in Sarawak, Malaysia. Among the 2,461 data collected, 741 were hypertensive (30.1%) and 1720 were normal (69.9%). During the data gathering process, eleven anthropometric measurements and sociodemographic data were collected. The variable selection procedure in the methodology proposed selected five parameters: weight, weighttoheight ratio (WHtR), age, sex, and ethnicity, as the input of the network model. The developed MLP model with a single hidden layer of 50 hidden neurons managed to achieve a sensitivity of 0.41, specificity of 0.91, precision of 0.65, Fscore of 0.50, accuracy of 0.76, and Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) of 0.75 using the imbalanced data set. Analyzing the performance metrics obtained from the training, validation and testing data sets show that the developed network model is wellgeneralized. Using Bayes’ Theorem, an adolescent classified as hypertensive using this created model has a 66.2% likelihood of having hypertension in the Sarawak adolescent population, which has a hypertension prevalence of 30.1%. When the prevalence of hypertension in the Sarawak population was increased to 50%, the developed model could predict an adolescent having hypertension with an 82.0% chance, whereas when the prevalence of hypertension was reduced to 10%, the developed model could only predict true positive hypertension with a 33.6% chance. With the sensitivity of the model increasing to 65% and 90% while retaining a specificity of 91%, the true positivity of an adolescent being hypertension would be 75.7% and 81.2%, respectively, according to Bayes’ Theorem. The findings show that simple anthropometric measurements paired with sociodemographic data are feasible to be used to classify hypertension in adolescents using the developed MLP model in Sarawak adolescent population with modest hypertension prevalence. However, a model with higher sensitivity and specificity is required for better positive hypertension predictive value when the prevalence is low. We conclude that the developed classification model could serve as a quick and easy preliminary warning tool for screening highrisk adolescents of developing hypertension.
Item Type:  Article 

Uncontrolled Keywords:  Multilayer perceptron , MLP , Classify Hypertension , Sarawak , Malaysia 
Subjects:  Q Science > QA Mathematics > QA1939 Mathematics > QA7190 Instruments and machines > QA75.576.95 Electronic computers. Computer science 
Divisions:  FACULTY > Faculty of Computing and Informatics 
Depositing User:  SITI AZIZAH BINTI IDRIS  
Date Deposited:  16 Jun 2022 16:08 
Last Modified:  16 Jun 2022 16:08 
URI:  https://eprints.ums.edu.my/id/eprint/32830 
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